icnet-camvid-ava-sparse-30-0001

Use Case and High-Level Description

A trained model of ICNet for fast semantic segmentation, trained on the CamVid* dataset from scratch using the TensorFlow* framework. The trained model has 30% sparsity (ratio of 0's within all the convolution kernel weights). For more details about the original floating point model, check out the paper.

The model input is a blob that consists of a single image of "1x3x720x960" in BGR order. The pixel values are integers in the [0, 255] range.

The model output for icnet-camvid-ava-sparse-30-0001 is the predicted class index of each input pixel belonging to one of the 12 classes of the CamVid dataset.

Specification

Metric Value
GFlops 151.82Bn
MParams 25.45
Source framework TensorFlow*

Accuracy

The quality metrics were calculated on the CamVid* validation dataset. The 'unlabeled' class had been ignored during metrics calculation.

Metric Value
mIoU 69.99%

Performance

Input

Image, shape - 1,3,720,960, format is B,C,H,W where:

Channel order is BGR.

Output

Semantic segmentation class prediction map, shape - 1,720,960, output data format is B,H,W where:

containing the class prediction result of each pixel.

Legal Information

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